Biomarker development for Huntington's disease

Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial

BACKGROUND

In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.

OBJECTIVES

To improve stratification of Huntington's disease individuals for clinical trials.

METHODS

We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.

RESULTS

The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).

CONCLUSIONS

This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Ganglioside-focused Glycan Array Reveals Abnormal Anti-GD1b Auto-antibody in Plasma of Preclinical Huntington's Disease

Huntington's disease (HD) is a progressive and devastating neurodegenerative disease marked by inheritable CAG nucleotide expansion. For offspring of HD patients carrying abnormal CAG expansion, biomarkers that predict disease onset are crucially important but still lacking. Alteration of brain ganglioside patterns has been observed in the pathology of patients carrying HD. Here, by using a novel and sensitive ganglioside-focused glycan array, we examined the potential of anti-glycan auto-antibodies for HD. In this study, we collected plasma from 97 participants including 42 control (NC), 16 pre-manifest HD (pre-HD), and 39 HD cases and measured the anti-glycan auto-antibodies by a novel ganglioside-focused glycan array. The association between plasma anti-glycan auto-antibodies and disease progression was analyzed using univariate and multivariate logistic regression. The disease-predictive capacity of anti-glycan auto-antibodies was further investigated by receiver operating characteristic (ROC) analysis. We found that anti-glycan auto-antibodies were generally higher in the pre-HD group when compared to the NC and HD groups. Specifically, anti-GD1b auto-antibody demonstrated the potential for distinguishing between pre-HD and control groups. Moreover, in combination with age and the number of CAG repeat, the level of anti-GD1b antibody showed excellent predictability with an area under the ROC curve (AUC) of 0.95 to discriminate between pre-HD carriers and HD patients. With glycan array technology, this study demonstrated abnormal auto-antibody responses that showed temporal changes from pre-HD to HD.

Validating Automated Segmentation Tools in the Assessment of Caudate Atrophy in Huntington's Disease

Background: Neuroimaging shows considerable promise in generating sensitive and objective outcome measures for therapeutic trials across a range of neurodegenerative conditions. For volumetric measures the current gold standard is manual delineation, which is unfeasible for samples sizes required for large clinical trials. Methods: Using a cohort of early Huntington's disease (HD) patients (n = 46) and controls (n = 35), we compared the performance of four automated segmentation tools (FIRST, FreeSurfer, STEPS, MALP-EM) with manual delineation for generating cross-sectional caudate volume, a region known to be vulnerable in HD. We then examined the effect of each of these baseline regions on the ability to detect change over 15 months using the established longitudinal Caudate Boundary Shift Integral (cBSI) method, an automated longitudinal pipeline requiring a baseline caudate region as an input. Results: All tools, except Freesurfer, generated significantly smaller caudate volumes than the manually derived regions. Jaccard indices showed poorer levels of overlap between each automated segmentation and manual delineation in the HD patients compared with controls. Nevertheless, each method was able to demonstrate significant group differences in volume (p < 0.001). STEPS performed best qualitatively as well as quantitively in the baseline analysis. Caudate atrophy measures generated by the cBSI using automated baseline regions were largely consistent with those derived from a manually segmented baseline, with STEPS providing the most robust cBSI values across both control and HD groups. Conclusions: Atrophy measures from the cBSI were relatively robust to differences in baseline segmentation technique, suggesting that fully automated pipelines could be used to generate outcome measures for clinical trials.

TSPO-PET imaging using [18F]PBR06 is a potential translatable biomarker for treatment response in Huntington's disease: preclinical evidence with the p75NTR ligand LM11A-31

Huntington's disease (HD) is an inherited neurodegenerative disorder that has no cure. HD therapeutic development would benefit from a non-invasive translatable biomarker to track disease progression and treatment response. A potential biomarker is using positron emission tomography (PET) imaging with a translocator protein 18 kDa (TSPO) radiotracer to detect microglial activation, a key contributor to HD pathogenesis. The ability of TSPO-PET to identify microglial activation in HD mouse models, essential for a translatable biomarker, or therapeutic efficacy in HD patients or mice is unknown. Thus, this study assessed the feasibility of utilizing PET imaging with the TSPO tracer, [18F]PBR06, to detect activated microglia in two HD mouse models and to monitor response to treatment with LM11A-31, a p75NTR ligand known to reduce neuroinflammation in HD mice. [18F]PBR06-PET detected microglial activation in striatum, cortex and hippocampus of vehicle-treated R6/2 mice at a late disease stage and, notably, also in early and mid-stage symptomatic BACHD mice. After oral administration of LM11A-31 to R6/2 and BACHD mice, [18F]PBR06-PET discerned the reductive effects of LM11A-31 on neuroinflammation in both HD mouse models. [18F]PBR06-PET signal had a spatial distribution similar to ex vivo brain autoradiography and correlated with microglial activation markers: increased IBA-1 and TSPO immunostaining/blotting and striatal levels of cytokines IL-6 and TNFα. These results suggest that [18F]PBR06-PET is a useful surrogate marker of therapeutic efficacy in HD mice with high potential as a translatable biomarker for preclinical and clinical HD trials.

New directions in therapeutics for Huntington disease

Huntington disease (HD) is an autosomal dominantly inherited neurodegenerative disease that affects motor, cognitive and psychiatric functions, and ultimately leads to death. The pathology of the disease is based on an expansion of CAG repeats in exon 1 of the huntingtin gene on chromosome 4, which produces a mutant huntingtin protein (mHtt). This protein is involved in neurotoxicity and brain atrophy, and can form β-sheets and abnormal mHtt aggregates. Currently, there are no approved effective treatments for HD, although tetrabenazine (Xenazine™) and deutetrabenazine (AUSTEDO™) have been approved for treatment of the motor symptom chorea in HD. This literature review aims to address the latest research on promising therapeutics based on influencing the hypothesized pathological mechanisms.

Quantitative Electroencephalographic Biomarkers in Preclinical and Human Studies of Huntington's Disease: Are They Fit-for-Purpose for Treatment Development?

A major focus in development of novel therapies for Huntington's disease (HD) is identification of treatments that reduce the burden of mutant huntingtin (mHTT) protein in the brain. In order to identify and test the efficacy of such therapies, it is essential to have biomarkers that are sensitive to the effects of mHTT on brain function to determine whether the intervention has been effective at preventing toxicity in target brain systems before onset of clinical symptoms. Ideally, such biomarkers should have a plausible physiologic basis for detecting the effects of mHTT, be measureable both in preclinical models and human studies, be practical to measure serially in clinical trials, and be reliably measurable in HD gene expansion carriers (HDGECs), among other features. Quantitative electroencephalography (qEEG) fulfills many of these basic criteria of a "fit-for-purpose" biomarker. qEEG measures brain oscillatory activity that is regulated by the brain structures that are affected by mHTT in premanifest and early symptom individuals. The technology is practical to implement in the laboratory and is well tolerated by humans in clinical trials. The biomarkers are measureable across animal models and humans, with findings that appear to be detectable in HDGECs and translate across species. We review here the literature on recent developments in both preclinical and human studies of the use of qEEG biomarkers in HD, and the evidence for their usefulness as biomarkers to help guide development of novel mHTT lowering treatments.

Correlations of Behavioral Deficits with Brain Pathology Assessed through Longitudinal MRI and Histopathology in the HdhQ150/Q150 Mouse Model of Huntington's Disease

A variety of mouse models have been developed that express mutant huntingtin (mHTT) leading to aggregates and inclusions that model the molecular pathology observed in Huntington's disease. Here we show that although homozygous HdhQ150 knock-in mice developed motor impairments (rotarod, locomotor activity, grip strength) by 36 weeks of age, cognitive dysfunction (swimming T maze, fear conditioning, odor discrimination, social interaction) was not evident by 94 weeks. Concomitant to behavioral assessments, T2-weighted MRI volume measurements indicated a slower striatal growth with a significant difference between wild type (WT) and HdhQ150 mice being present even at 15 weeks. Indeed, MRI indicated significant volumetric changes prior to the emergence of the "clinical horizon" of motor impairments at 36 weeks of age. A striatal decrease of 27% was observed over 94 weeks with cortex (12%) and hippocampus (21%) also indicating significant atrophy. A hypothesis-free analysis using tensor-based morphometry highlighted further regions undergoing atrophy by contrasting brain growth and regional neurodegeneration. Histology revealed the widespread presence of mHTT aggregates and cellular inclusions. However, there was little evidence of correlations between these outcome measures, potentially indicating that other factors are important in the causal cascade linking the molecular pathology to the emergence of behavioral impairments. In conclusion, the HdhQ150 mouse model replicates many aspects of the human condition, including an extended pre-manifest period prior to the emergence of motor impairments.

Huntington disease: a single-gene degenerative disorder of the striatum

Huntington disease (HD) is an autosomal dominant, neurodegenerative disorder with a primary etiology of striatal pathology. The Huntingtin gene (HTT) has a unique feature of a DNA trinucleotide (triplet) repeat, with repeat length ranging from 10 to 35 in the normal population. Repeat lengths between 36 and 39 cause HD at reduced penetrance (some will get the disease, others won't) and when expanded to 40 or more repeats (mHTT), causes HD at full penetrance (every person with this length or beyond will definitely develop the disease). The symptoms of HD may be motor, cognitive, and psychiatric, and are consistent with the pathophysiology of frontostriatal circuitry malfunction. Expressed ubiquitously and throughout the entire life cycle (development through adulthood), mHTT causes initial dysfunction and eventual death of a specific cell population within the striatum. Although all areas of the brain are eventually affected, the primary pathology of the disease is regionally specific. As a single-gene disorder, HD has the distinction of having the potential of treatment that is aimed directly at the known pathogenic mechanism by gene silencing, providing hope for neuroprotection and ultimately, prevention.

Potential biomarkers to follow the progression and treatment response of Huntington's disease

Disatnik et al. identify mitochondrial DNA levels, 8-OHdG, and inflammation factors as potential peripheral biomarkers to follow progression and treatment response of Huntington’s disease.

Huntington’s disease (HD) is a rare genetic disease caused by expanded polyglutamine repeats in the huntingtin protein resulting in selective neuronal loss. Although genetic testing readily identifies those who will be affected, current pharmacological treatments do not prevent or slow down disease progression. A major challenge is the slow clinical progression and the inability to biopsy the affected tissue, the brain, making it difficult to design short and effective proof of concept clinical trials to assess treatment benefit. In this study, we focus on identifying peripheral biomarkers that correlate with the progression of the disease and treatment benefit. We recently developed an inhibitor of pathological mitochondrial fragmentation, P110, to inhibit neurotoxicity in HD. Changes in levels of mitochondrial DNA (mtDNA) and inflammation markers in plasma, a product of DNA oxidation in urine, mutant huntingtin aggregates, and 4-hydroxynonenal adducts in muscle and skin tissues were all noted in HD R6/2 mice relative to wild-type mice. Importantly, P110 treatment effectively reduced the levels of these biomarkers. Finally, abnormal levels of mtDNA were also found in plasma of HD patients relative to control subjects. Therefore, we identified several potential peripheral biomarkers as candidates to assess HD progression and the benefit of intervention for future clinical trials.

Biomarkers of Brain Structure and Function for Neurodegenerative Disorders: Are They Adequate for Go/No Go Decisions in Drug Development?

Given the high risk of developing drugs for neurodegenerative diseases if post-phase I decisions to go into efficacy studies were made with quantitative knowledge of an agent's action in brain, the risks should be diminished. Furthermore, if biomarkers were compelling, they could be utilized during a lengthy trial as an early measure of futility. What follows is one perspective on the adequacy of current and emerging measures to be applied to such decision making.

Challenges of Huntington's disease and quest for therapeutic biomarkers

Huntington's disease (HD) is the most common inherited neurodegenerative disorder among polyglutamine (polyQ) diseases caused by cytosine-adenine-guanine repeat expansion in exon 1 of the huntingtin gene whose translation results in polyQ stretch in the N-terminus of the huntingtin protein (HD protein). This mutation significantly affects huntingtin conformation, proteolysis, PTMs, as well as its ability to bind interacting proteins. As a consequence, a variety of cellular mechanisms such as transcription, mitochondrial energy metabolism, axonal transport, neuronal vulnerability to oxidative stress, neurotransmission, and immune response are altered and involved in the pathogenesis of HD. Promising candidate molecular biomarkers of HD have emerged from proteomic studies. Recent analyses focused on HD protein itself, its PTM, and interacting proteins, which are of great importance for disease course. Furthermore, brain, body fluids, and immune system are intensively studied in order to search for additional proteins with a view to their use as a biomarker(s) or set of biomarkers in clinical trials in HD translational research.